import numpy as np
from typing import Dict, Any, Optional, List, Tuple, Union

from .base import CompressionMethod
import torch
import torch.nn as nn


class Quantization(CompressionMethod):
    """实现权重量化的压缩方法"""

    def __init__(self, config: Optional[Dict] = None):
        """
        初始化量化方法

        Args:
            config: 量化配置参数，包括：
                - bits: 量化位宽 (int, 默认: 8)
                - scheme: 量化方案 (str, 'symmetric'或'asymmetric', 默认: 'symmetric')
                - per_channel: 是否按通道量化 (bool, 默认: False)
                - dtype: 量化数据类型 (str, 默认: 'int')
        """
        super().__init__(config)
        self.bits = self.config.get("bits", 8)
        self.scheme = self.config.get("scheme", "symmetric")
        self.per_channel = self.config.get("per_channel", False)
        self.dtype = self.config.get("dtype", "int")

        self.logger.info(f"Initialized {self.bits}-bit {self.scheme} quantization")

    def compress(self, layer: Any) -> Any:
        """
        对层的权重进行量化

        Args:
            layer: 要量化的层

        Returns:
            量化后的层
        """
        self.logger.info(f"Applying {self.bits}-bit quantization to layer")

        try:
            # 这里的实现是框架无关的伪代码
            # 实际实现应由特定框架的适配器处理
            if hasattr(layer, "weight") and layer.weight is not None:
                # 记录原始权重用于评估
                self.original_weight = layer.weight.clone() if hasattr(layer.weight, "clone") else layer.weight

                # 实现量化逻辑
                if self.scheme == "symmetric":
                    layer = self._symmetric_quantize(layer)
                else:
                    layer = self._asymmetric_quantize(layer)

                self.logger.info("Quantization applied successfully")
            else:
                self.logger.warning("Layer has no weights to quantize")

            return layer

        except Exception as e:
            self.logger.error(f"Quantization failed: {str(e)}")
            import traceback
            self.logger.error(traceback.format_exc())
            # 出错时返回原始层
            return layer

    def _symmetric_quantize(self, layer: Any) -> Any:
        """对称量化实现"""
        try:
            # 计算量化范围
            n_steps = 2 ** (self.bits - 1) - 1  # 对称量化的步数

            # 获取设备信息
            device = layer.weight.device
            requires_grad = layer.weight.requires_grad  # 保存原始requires_grad状态

            # 暂时禁用梯度跟踪
            with torch.no_grad():
                # 创建新的模块以替换旧模块
                if isinstance(layer, nn.Conv2d):
                    new_layer = nn.Conv2d(
                        in_channels=layer.in_channels,
                        out_channels=layer.out_channels,
                        kernel_size=layer.kernel_size,
                        stride=layer.stride,
                        padding=layer.padding,
                        dilation=layer.dilation,
                        groups=layer.groups,
                        bias=layer.bias is not None,
                        padding_mode=layer.padding_mode
                    )
                elif isinstance(layer, nn.Linear):
                    new_layer = nn.Linear(
                        in_features=layer.in_features,
                        out_features=layer.out_features,
                        bias=layer.bias is not None
                    )
                else:
                    # 不支持的层类型，直接返回原层
                    return layer

                # 复制偏置（如果有）
                if layer.bias is not None:
                    new_layer.bias.data.copy_(layer.bias.data)

                # 对每个通道分别处理（如果启用了per_channel）
                if self.per_channel:
                    # 假设权重形状为[out_channels, in_channels, ...]
                    weight_copy = layer.weight.clone().detach()

                    for c in range(weight_copy.shape[0]):
                        # 获取该通道的权重
                        w_channel = weight_copy[c]

                        # 找出该通道的最大绝对值
                        max_abs_val = torch.max(torch.abs(w_channel))

                        # 计算量化比例
                        scale = max_abs_val / n_steps if max_abs_val > 0 else torch.tensor(1e-10, device=device)

                        # 量化和反量化
                        w_quant = torch.round(w_channel / scale).clamp(-n_steps, n_steps)
                        weight_copy[c] = w_quant * scale

                    # 设置量化后的权重
                    new_layer.weight.data.copy_(weight_copy)

                    # 保存每个通道的缩放因子（对于调试和输出）
                    scales = []
                    for c in range(weight_copy.shape[0]):
                        w_channel = layer.weight[c]
                        max_abs_val = torch.max(torch.abs(w_channel))
                        scale = max_abs_val / n_steps if max_abs_val > 0 else torch.tensor(1e-10, device=device)
                        scales.append(scale.item())

                    # 添加量化信息作为层的属性
                    new_layer._quantized = True
                    new_layer._quant_bit_width = self.bits
                    new_layer._quant_scheme = self.scheme
                    new_layer._quant_scales = scales
                    new_layer._quant_per_channel = True

                else:
                    # 整个权重矩阵一起处理
                    weight_copy = layer.weight.clone().detach()

                    # 找出最大绝对值
                    max_abs_val = torch.max(torch.abs(weight_copy))

                    # 计算量化比例
                    scale = max_abs_val / n_steps if max_abs_val > 0 else torch.tensor(1e-10, device=device)

                    # 量化和反量化
                    w_quant = torch.round(weight_copy / scale).clamp(-n_steps, n_steps)
                    weight_dequant = w_quant * scale

                    # 设置量化后的权重
                    new_layer.weight.data.copy_(weight_dequant)

                    # 添加量化信息作为层的属性
                    new_layer._quantized = True
                    new_layer._quant_bit_width = self.bits
                    new_layer._quant_scheme = self.scheme
                    new_layer._quant_scale = scale.item()
                    new_layer._quant_per_channel = False

                # 记录真实存储大小
                new_layer._quant_storage_size = layer.weight.numel() * self.bits / 8

                # 还原requires_grad状态
                new_layer.weight.requires_grad = requires_grad
                if new_layer.bias is not None and layer.bias is not None:
                    new_layer.bias.requires_grad = layer.bias.requires_grad

            self.logger.info("Symmetric quantization applied successfully")
            return new_layer

        except Exception as e:
            self.logger.error(f"Symmetric quantization failed: {str(e)}")
            import traceback
            self.logger.error(traceback.format_exc())
            return layer

    def _asymmetric_quantize(self, layer: Any) -> Any:
        """非对称量化实现"""
        try:
            # 计算量化范围
            n_steps = 2 ** self.bits - 1  # 非对称量化的步数

            # 获取设备信息
            device = layer.weight.device
            requires_grad = layer.weight.requires_grad  # 保存原始requires_grad状态

            # 暂时禁用梯度跟踪
            with torch.no_grad():
                # 创建新的模块以替换旧模块
                if isinstance(layer, nn.Conv2d):
                    new_layer = nn.Conv2d(
                        in_channels=layer.in_channels,
                        out_channels=layer.out_channels,
                        kernel_size=layer.kernel_size,
                        stride=layer.stride,
                        padding=layer.padding,
                        dilation=layer.dilation,
                        groups=layer.groups,
                        bias=layer.bias is not None,
                        padding_mode=layer.padding_mode
                    )
                elif isinstance(layer, nn.Linear):
                    new_layer = nn.Linear(
                        in_features=layer.in_features,
                        out_features=layer.out_features,
                        bias=layer.bias is not None
                    )
                else:
                    # 不支持的层类型，直接返回原层
                    return layer

                # 复制偏置（如果有）
                if layer.bias is not None:
                    new_layer.bias.data.copy_(layer.bias.data)

                # 对每个通道分别处理（如果启用了per_channel）
                if self.per_channel:
                    # 假设权重形状为[out_channels, in_channels, ...]
                    weight_copy = layer.weight.clone().detach()

                    scales = []
                    zero_points = []

                    for c in range(weight_copy.shape[0]):
                        # 使用PyTorch函数而非NumPy函数
                        w_channel = weight_copy[c]
                        w_min = torch.min(w_channel)
                        w_max = torch.max(w_channel)

                        # 计算缩放和零点
                        scale = (w_max - w_min) / n_steps if w_max > w_min else torch.tensor(1e-10, device=device)
                        zero_point = torch.round(-w_min / scale) if scale > 0 else torch.tensor(0, device=device)

                        # 量化和反量化
                        w_quant = torch.round(w_channel / scale + zero_point).clamp(0, n_steps)
                        weight_copy[c] = (w_quant - zero_point) * scale

                        scales.append(scale.item())
                        zero_points.append(zero_point.item())

                    # 设置量化后的权重
                    new_layer.weight.data.copy_(weight_copy)

                    # 添加量化信息作为层的属性
                    new_layer._quantized = True
                    new_layer._quant_bit_width = self.bits
                    new_layer._quant_scheme = self.scheme
                    new_layer._quant_scales = scales
                    new_layer._quant_zero_points = zero_points
                    new_layer._quant_per_channel = True

                else:
                    # 整个权重矩阵一起处理
                    weight_copy = layer.weight.clone().detach()

                    # 计算最小和最大值
                    w_min = torch.min(weight_copy)
                    w_max = torch.max(weight_copy)

                    # 计算缩放和零点
                    scale = (w_max - w_min) / n_steps if w_max > w_min else torch.tensor(1e-10, device=device)
                    zero_point = torch.round(-w_min / scale) if scale > 0 else torch.tensor(0, device=device)

                    # 量化和反量化
                    w_quant = torch.round(weight_copy / scale + zero_point).clamp(0, n_steps)
                    weight_dequant = (w_quant - zero_point) * scale

                    # 设置量化后的权重
                    new_layer.weight.data.copy_(weight_dequant)

                    # 添加量化信息作为层的属性
                    new_layer._quantized = True
                    new_layer._quant_bit_width = self.bits
                    new_layer._quant_scheme = self.scheme
                    new_layer._quant_scale = scale.item()
                    new_layer._quant_zero_point = zero_point.item()
                    new_layer._quant_per_channel = False

                # 记录真实存储大小
                new_layer._quant_storage_size = layer.weight.numel() * self.bits / 8

                # 还原requires_grad状态
                new_layer.weight.requires_grad = requires_grad
                if new_layer.bias is not None and layer.bias is not None:
                    new_layer.bias.requires_grad = layer.bias.requires_grad

            self.logger.info("Asymmetric quantization applied successfully")
            return new_layer

        except Exception as e:
            self.logger.error(f"Asymmetric quantization failed: {str(e)}")
            import traceback
            self.logger.error(traceback.format_exc())
            return layer

    def evaluate(self, original: Any, compressed: Any) -> Dict:
        """
        评估量化前后的层性能

        Args:
            original: 原始层
            compressed: 量化后的层

        Returns:
            性能评估指标
        """
        metrics = {}

        try:
            # 计算权重误差
            if hasattr(original, "weight") and hasattr(compressed, "weight"):
                orig_w = original.weight.data.cpu().numpy()
                comp_w = compressed.weight.data.cpu().numpy()

                # 计算相对误差
                abs_err = np.mean(np.abs(orig_w - comp_w))
                rel_err = abs_err / (np.mean(np.abs(orig_w)) + 1e-8)

                metrics["absolute_error"] = float(abs_err)
                metrics["relative_error"] = float(rel_err)

                # 计算压缩率
                orig_size = orig_w.size * 4  # 假设原始为float32 (4字节)

                # 使用记录的量化存储大小
                if hasattr(compressed, '_quant_storage_size'):
                    comp_size = compressed._quant_storage_size
                else:
                    # 回退到基于位宽的估计
                    comp_size = compressed.weight.numel() * self.bits / 8

                metrics["original_size_bytes"] = float(orig_size)
                metrics["compressed_size_bytes"] = float(comp_size)
                metrics["compression_ratio"] = float(orig_size / comp_size)

            # 添加量化参数信息
            if hasattr(compressed, "_quantized"):
                if hasattr(compressed, "_quant_per_channel") and compressed._quant_per_channel:
                    # 按通道量化情况
                    metrics["quantization_params"] = {
                        "bits": compressed._quant_bit_width,
                        "scheme": compressed._quant_scheme,
                        "per_channel": True,
                    }
                    if hasattr(compressed, "_quant_scales"):
                        metrics["quantization_params"]["scales"] = compressed._quant_scales[:5]  # 只取前几个用于示例
                    if hasattr(compressed, "_quant_zero_points"):
                        metrics["quantization_params"]["zero_points"] = compressed._quant_zero_points[:5]  # 只取前几个
                else:
                    # 整体量化情况
                    metrics["quantization_params"] = {
                        "bits": compressed._quant_bit_width,
                        "scheme": compressed._quant_scheme,
                    }
                    if hasattr(compressed, "_quant_scale"):
                        metrics["quantization_params"]["scale"] = compressed._quant_scale
                    if hasattr(compressed, "_quant_zero_point"):
                        metrics["quantization_params"]["zero_point"] = compressed._quant_zero_point

        except Exception as e:
            self.logger.error(f"Evaluation failed: {str(e)}")
            import traceback
            self.logger.error(traceback.format_exc())

        return metrics

    def can_apply(self, layer_type: str) -> bool:
        """检查量化是否适用于该层类型"""
        # 量化通常适用于具有权重的层
        applicable_types = [
            "Conv1d", "Conv2d", "Conv3d",
            "Linear", "Dense",
            "LSTM", "GRU", "RNN",
            "Embedding",
            "BatchNorm1d", "BatchNorm2d", "BatchNorm3d",
            "LayerNorm"
        ]
        return layer_type in applicable_types

    @classmethod
    def get_default_params(cls) -> Dict:
        """获取默认量化参数"""
        return {
            "bits": 8,
            "scheme": "symmetric",
            "per_channel": False,
            "dtype": "int"
        }

    def get_expected_metrics(self, layer_info: Dict) -> Dict:
        """
        获取量化的预期性能影响

        Args:
            layer_info: 层的信息

        Returns:
            预期的性能指标变化
        """
        # 根据位宽估算精度影响和大小减少
        accuracy_impact = {
            8: 0.99,  # 8位量化通常保持99%精度
            6: 0.97,  # 6位量化通常保持97%精度
            4: 0.93,  # 4位量化可能导致7%精度损失
            2: 0.85  # 2位量化可能导致15%精度损失
        }

        # 根据位宽计算大小减少比例
        size_ratio = self.bits / 32.0  # 相对于float32

        # 量化通常对推理延迟有轻微的改进
        latency_ratio = 0.9 if self.bits <= 8 else 0.95

        # 构建预期指标
        return {
            "accuracy_ratio": accuracy_impact.get(self.bits, 0.9),
            "size_ratio": size_ratio,
            "latency_ratio": latency_ratio,
            "memory_ratio": size_ratio  # 内存减少与大小减少基本一致
        }